它是on-policy还是off-policy,为什么? 4)写出用第n步的值函数更新当前值函数的公式(1-step,2-step,n-step的意思)。当n的取值变大时,期望和方差分别变大、变小? 5)TD(λ)方法:当λ=0时实际上与哪种方法等价,λ=1呢? 6 基于值函数逼近的强化学习方法 6.1 基于值函数逼近的理论讲解 写出蒙特卡洛、TD和T...
step() ## update 网络 cur_agent.actor_optimizer.zero_grad() ## 策略网络参数梯度置 0 的 cur_actor_out = cur_agent.actor(obs[i_agent]) ## 当前智能体策略网络根据当前状态,给出动作的概率 cur_act_vf_in = gumbel_softmax(cur_actor_out) ## 动作采样的 all_actor_acs = [] for i, (...
nextStates.append(state.nextState(i, j, self.symbol).getHash())ifnp.random.binomial(1, self.exploreRate): np.random.shuffle(nextPositions)#Not sure if truncating is the best way to deal with exploratory step#Maybe it's better to only skip this step rather than forget all the historyself...
action=env.action_space.sample() state, reward, done, info=env.step(action)ifdone:print('End game! Reward:', reward)print('You won :)\n')ifreward > 0elseprint('You lost :(\n')break (9, 10, False) End game! Reward: -1.0 You lost :( (11, 3, False) (12, 3, False) (19...
(-decay_rate*step) if explore_p > np.random.rand(): # Make a random action action = env.action_space.sample() else: # Get action from Q-network feed = {mainQN.inputs_: state.reshape((1, *state.shape))} Qs = sess.run(mainQN.output, feed_dict=feed) action = np.argmax(Qs)...
defupdate_Q(Qsa, Qsa_next, reward, alpha, gamma):"""updates the action-value function estimate using the most recent time step"""returnQsa + (alpha * (reward + (gamma * Qsa_next) -Qsa)) defepsilon_greedy_probs(env, Q_s, i_episode, eps=None):"""obtains the action probabiliti...